Comparing statistical information

In the beginning of the project we collected and shared plenty of statistical information about our libraries and universities. The plan was to compare the activities and results. Areas were

  • Library areas, facilities and equipment
  • Services for the public, including loan, ILL and user training
  • Collection management, bibliographic records
  • Institutional repository
  • Library staff, both number and staff training
  • Financial data

Statistical data will give more value when comparing with others or with oneself, over time, but which statistics can be compared? One example is loans. Number of loans is easy to compare, and the numbers can be extracted from the library systems. For 2013 we have the following statistics for loans, visits to the libraries and size of collections. But we miss important data, e.g. on downloads of articles, and use of e-books.


*) Include renewals, for NTNU is for example the number for first time loan is about 50%.

**) Apply to the whole institutions, not just the medical libraries. Medicine has focus on articles more than books, so these numbers are not valid for medicine.

How to compare?

We can observe that NTNU and UCL have quite similar number of loans, and almost twice as much as UEF, at the same time  NTNU and UCL have almost twice as many visits as UEF. And the inter library loan at NTNU is three times as high as at UCL. Can we see any correlation at all? It is also easy to compare interlibrary loans. Use of collections in medical libraries tend to have a predominance on articles, at the same time prices of electronic journals are increasing more then price of books. This mean that no library are able to have all the journals needed in their collection. So ILL can say something about the quality of the library collection – and also about the size of the media budget. An example: the NTNU Library (BMH) use about 2.5 % of the media budget on buying copies, a very small amount when more than 90% of the budget is used on electronic resources and journals.

Many element affect the statistics and the use of library services. Number of loans must be seen in relation to the size of the universities. The NTNU part of BMH serves 3000 students and 11000 staff members at NTNU and St. Olavs Hospital. UCL serves about 6000 medical students and 760 academic and research personnel, and the UEF Library serves a total of 3000 university staff members and 15 000 students (about 1400 medical and dental students). Other elements affecting statistics are size of the collection, how updated is the collection, acquisitions per year, number of users, the amount of e-books and e-journals, the number of printed books replaced by e-books and so on.

Should we be able to compare, we must use indicators; this will be discussed  in a separate blog post. Examples of other useful indicators could be

  • Number of e-journals / download of articles
  • Number of e-books / number of pages read
  • Loans from library collection / ILL
  • Loans from library collection / number of students

As an example we can calculate the relation between visits to the library and loans. NTNU have 0.42 loans per visit, while at UCL and UEF are respectively 0.17 and 0.1 loans per visit. If this is done for several years, one can get a picture of how user activity in the library develops over a period of time. And then it also gives more meaning to comparing libraries.

Follow the money

When we started collecting data, we did not know how to compare. We have lots of data, but not necessarily the most interesting or useful data. It became clear that though we all three are medical/health sciences libraries that serve both faculty and university hospital and also other users, we are neither organized nor financed in the same way. Due to these differences  it is difficult to compare economic data. Yet, it would be useful, and the library directors are keen to compare both financial and other data. While visiting the three libraries, we had discussions with the  library directors and got suggestions for further work on statistics and data. In Trondheim we were encouraged to measure the impact of the library, and to look at the connections between quantitative and qualitative indicators. In Louvain-la-Neuve we talked about library statistics and economics, and that it is important to seek out at least some comparable indicators nationally and internationally. In Kuopio we discussed statistics and other data as useful background information.

Next step for our project is to find indicators for library performance. More on this topic in another blog post.